The main contribution of this work is that we create a connection between exploration methods and model-based reinforcement learning. We present a method of maximum-entropy exploration for model-based reinforcement learning agents and build a MaxEnt Dreamer that improves the performance to model-based Dreamer [1]}. We also introduce some additional modifications to improve the stability and perform an ablations study to show their effectiveness. Experimental results show that our exploration method and our modifications significantly improve the performance of Dreamer. We also perform an ablation study to show that it is indeed exploration techniques that contribute the majority of the improvement.
